The overall goal of the proposed research is to achieve predictive monitoring of the acute ketoacidotic state by developing an on-line dynamic model and an algorithm to update it in accord with the actual clinical course by repetitive adaptation of certain model parameters. The model will accept standard clinical measurements and estimate unmeasurable physiological states as well as predict the time course of all measurable states. Clinical measurements and proposed therapy would be entered at random intervals and the physician could query the computer for physiological parameters, predicted course, or cumulative past course. The metabolic portion of the model will consist of aggregated versions of the intermediary metabolism of glucose, lipids, and amino acids for liver, muscle, and adipose tissue. The spatial distribution of metabolites will be modelled by compartmental analysis. The fluid and solute distribution portion of the model treats the intracellular, extracellular, and vascular distribution of certain metabolic solutes (glucose, urea, etc.), electrolytes, hormones, and water.